The Stability Of High Speed Milling And Cutting Parameters Optimization Study

Stable cutting is the prerequisite to ensure efficient and high-precision machining, which is the basis for the optimization of process parameters and improvement of processing efficiency. In this article, the study focused on the dynamic modeling, the stability prediction and the surface processing quality in milling process, aiming to provide a guidance for the achievement of chatter-free high-speed milling and the optimization of processing parameters.Considering the changes in the thickness of the dynamic cutting process, a dynamic milling force model was established. By establishing the regenerative chatter analytical model, the milling process stability was predicted and the stability lobes diagram were established. The frequency characteristics of the signal in the milling process were studied, and the stability of small-radial-depth cut was predictied with the semi-discretization method. Through simulation analysis, factors which may affect milling stability were studied.In this work, high-speed milling chatter tests were conducted on Aluminum7050. The cutting force coefficients were obtained by experiments, and the modal parameters obtained by hammer modal tests. The experimental and simulation results demonstrate the milling force model and the accuracy of the milling force coefficients. By analyzing changes of the milling force and the frequency components of the signal spectrum in cutting process, achieved recognition of chatter. The simulation predicted and experimental results were compared, and the accuracy of the stability forecasting model was verified. Based on chatter recognition technology, an optimization strategy of chatter suppression by adjusting the cutting parameters was given, and with time domain simulation analysis, its feasibility was tested and verified.High-speed milling tests were also carried out with titanium alloy TC4, the law of the impact that cutting parameters may do on surface roughness were analyzed through orthogonal experiments and single factor tests. Based on BP neural network, surface roughness prediction model was established, and finally combined with the study findings on milling stability and surface roughness, achieved optimization of the milling parameters by genetic optimization algorithm.